437 citations · 1.4k across the 31 of their papers we have counts for
5 papers · 1 filter
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension
Sheng Zhang, Xiaodong Liu, Jingjing Liu +3
We present a large-scale dataset, ReCoRD, for machine reading comprehension requiring commonsense reasoning. Experiments on this dataset demonstrate that the performance of state-o…
Stochastic Answer Networks for SQuAD 2.0
Xiaodong Liu, Wei Li, Yuwei Fang +3
This paper presents an extension of the Stochastic Answer Network (SAN), one of the state-of-the-art machine reading comprehension models, to be able to judge whether a question is…
Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension
Yichong Xu, Xiaodong Liu, Yelong Shen +2
We propose a multi-task learning framework to learn a joint Machine Reading Comprehension (MRC) model that can be applied to a wide range of MRC tasks in different domains. Inspire…
Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models
Minjia Zhang, Xiaodong Liu, Wenhan Wang +2
Neural language models (NLMs) have recently gained a renewed interest by achieving state-of-the-art performance across many natural language processing (NLP) tasks. However, NLMs a…
Stochastic Answer Networks for Natural Language Inference
Xiaodong Liu, Kevin Duh, Jianfeng Gao
We propose a stochastic answer network (SAN) to explore multi-step inference strategies in Natural Language Inference. Rather than directly predicting the results given the inputs,…